Regulatory factor X 7 (Rfx7) is an uncharacterized transcription factor belonging to a family involved in ciliogenesis and immunity. Here, we found that deletion of Rfx7 leads to a decrease in natural killer (NK) cell maintenance and immunity in vivo. Genomic approaches showed that Rfx7 coordinated a transcriptional network controlling cell metabolism. Rfx7–/– NK lymphocytes presented increased size, granularity, proliferation, and energetic state, whereas genetic reduction of mTOR activity mitigated those defects. Notably, Rfx7-deficient NK lymphocytes were rescued by interleukin 15 through engagement of the Janus kinase (Jak) pathway, thus revealing the importance of this signaling for maintenance of such spontaneously activated NK cells. Rfx7 therefore emerges as a novel transcriptional regulator of NK cell homeostasis and metabolic quiescence.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from $8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Emery, P. et al. A consensus motif in the RFX DNA binding domain and binding domain mutants with altered specificity. Mol. Cell. Biol. 16, 4486–4494 (1996).

  2. 2.

    Aftab, S., Semenec, L., Chu, J. S. & Chen, N. Identification and characterization of novel human tissue-specific RFX transcription factors. BMC Evol. Biol. 8, 226 (2008).

  3. 3.

    Dorn, A. et al. Conserved major histocompatibility complex class II boxes: X and Y--are transcriptional control elements and specifically bind nuclear proteins. Proc. Natl Acad. Sci. USA 84, 6249–6253 (1987).

  4. 4.

    Badis, G. et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).

  5. 5.

    Chung, M. I. et al. Coordinated genomic control of ciliogenesis and cell movement by RFX2. eLife 3, e01439 (2014).

  6. 6.

    Kistler, W. S. et al. RFX2 is a major transcriptional regulator of spermiogenesis. PLoS. Genet. 11, e1005368 (2015).

  7. 7.

    Blackshear, P. J. et al. Graded phenotypic response to partial and complete deficiency of a brain-specific transcript variant of the winged helix transcription factor RFX4. Development 130, 4539–4552 (2003).

  8. 8.

    Ashique, A. M. et al. The Rfx4 transcription factor modulates Shh signaling by regional control of ciliogenesis. Sci. Signal. 2, ra70 (2009).

  9. 9.

    Bonnafe, E. et al. The transcription factor RFX3 directs nodal cilium development and left-right asymmetry specification. Mol. Cell. Biol. 24, 4417–4427 (2004).

  10. 10.

    Baas, D. et al. A deficiency in RFX3 causes hydrocephalus associated with abnormal differentiation of ependymal cells. Eur. J. Neurosci. 24, 1020–1030 (2006).

  11. 11.

    Ait-Lounis, A. et al. Novel function of the ciliogenic transcription factor RFX3 in development of the endocrine pancreas. Diabetes 56, 950–959 (2007).

  12. 12.

    Smith, S. B. et al. Rfx6 directs islet formation and insulin production in mice and humans. Nature 463, 775–780 (2010).

  13. 13.

    Reith, W. & Mach, B. The bare lymphocyte syndrome and the regulation of MHC expression. Annu. Rev. Immunol. 19, 331–373 (2001).

  14. 14.

    Ludigs, K. et al. NLRC5 exclusively transactivates MHC class I and related genes through a distinctive SXY module. PLoS. Genet. 11, e1005088 (2015).

  15. 15.

    Manojlovic, Z., Earwood, R., Kato, A., Stefanovic, B. & Kato, Y. RFX7 is required for the formation of cilia in the neural tube. Mech. Dev. 132, 28–37 (2014).

  16. 16.

    Bullinger, L. et al. Identification of acquired copy number alterations and uniparental disomies in cytogenetically normal acute myeloid leukemia using high-resolution single-nucleotide polymorphism analysis. Leukemia 24, 438–449 (2010).

  17. 17.

    Crowther-Swanepoel, D. et al. Common variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 influence chronic lymphocytic leukemia risk. Nat. Genet. 42, 132–136 (2010).

  18. 18.

    Slager, S. L. et al. Common variation at 6p21.31 (BAK1) influences the risk of chronic lymphocytic leukemia. Blood 120, 843–846 (2012).

  19. 19.

    Berndt, S. I. et al. Genome-wide association study identifies multiple risk loci for chronic lymphocytic leukemia. Nat. Genet. 45, 868–876 (2013).

  20. 20.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

  21. 21.

    Yau, C. et al. A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer. Breast Cancer Res. 12, R85 (2010).

  22. 22.

    Rogers, L. M., Olivier, A. K., Meyerholz, D. K. & Dupuy, A. J. Adaptive immunity does not strongly suppress spontaneous tumors in a Sleeping Beauty model of cancer. J. Immunol. 190, 4393–4399 (2013).

  23. 23.

    Rusiniak, M. E., Kunnev, D., Freeland, A., Cady, G. K. & Pruitt, S. C. Mcm2 deficiency results in short deletions allowing high resolution identification of genes contributing to lymphoblastic lymphoma. Oncogene 31, 4034–4044 (2012).

  24. 24.

    Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2018).

  25. 25.

    Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130 (2009).

  26. 26.

    Boyman, O., Kovar, M., Rubinstein, M. P., Surh, C. D. & Sprent, J. Selective stimulation of T cell subsets with antibody-cytokine immune complexes. Science 311, 1924–1927 (2006).

  27. 27.

    Rota, G. et al. T Cell priming by activated nlrc5-deficient dendritic cells is unaffected despite partially reduced MHC class I levels. J. Immunol. 196, 2939–2946 (2016).

  28. 28.

    Yang, M. et al. NK cell development requires Tsc1-dependent negative regulation of IL-15-triggered mTORC1 activation. Nat. Commun. 7, 12730 (2016).

  29. 29.

    Dai, Q. et al. mTOR/Raptor signaling is critical for skeletogenesis in mice through the regulation of Runx2 expression. Cell. Death Differ. 24, 1886–1899 (2017).

  30. 30.

    Polak, P. et al. Adipose-specific knockout of raptor results in lean mice with enhanced mitochondrial respiration. Cell. Metab. 8, 399–410 (2008).

  31. 31.

    Inoki, K. et al. mTORC1 activation in podocytes is a critical step in the development of diabetic nephropathy in mice. J. Clin. Invest. 121, 2181–2196 (2011).

  32. 32.

    Sathe, P. et al. Innate immunodeficiency following genetic ablation of Mcl1 in natural killer cells. Nat. Commun. 5, 4539 (2014).

  33. 33.

    Doucey, M. A. et al. Cis association of Ly49A with MHC class I restricts natural killer cell inhibition. Nat. Immunol. 5, 328–336 (2004).

  34. 34.

    The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017).

  35. 35.

    Yang, K., Neale, G., Green, D. R., He, W. & Chi, H. The tumor suppressor Tsc1 enforces quiescence of naive T cells to promote immune homeostasis and function. Nat. Immunol. 12, 888–897 (2011).

  36. 36.

    Brugarolas, J. et al. Regulation of mTOR function in response to hypoxia by REDD1 and the TSC1/TSC2 tumor suppressor complex. Genes. Dev. 18, 2893–2904 (2004).

  37. 37.

    Boros, K., Lacaud, G. & Kouskoff, V. The transcription factor Mxd4 controls the proliferation of the first blood precursors at the onset of hematopoietic development in vitro. Exp. Hematol. 39, 1090–1100 (2011).

  38. 38.

    Hurlin, P. J. et al. Mad3 and Mad4: novel Max-interacting transcriptional repressors that suppress c-myc dependent transformation and are expressed during neural and epidermal differentiation. EMBO J. 14, 5646–5659 (1995).

  39. 39.

    Wei, H. et al. Cutting edge: Foxp1 controls naive CD8+ T cell quiescence by simultaneously repressing key pathways in cellular metabolism and cell cycle Progression. J. Immunol. 196, 3537–3541 (2016).

  40. 40.

    Zhu, Z. et al. PI3K is negatively regulated by PIK3IP1, a novel p110 interacting protein. Biochem. Biophys. Res. Commun. 358, 66–72 (2007).

  41. 41.

    Lauth, M. et al. DYRK1B-dependent autocrine-to-paracrine shift of Hedgehog signaling by mutant RAS. Nat. Struct. Mol. Biol. 17, 718–725 (2010).

  42. 42.

    Keramati, A. R. et al. A form of the metabolic syndrome associated with mutations in DYRK1B. N. Engl. J. Med. 370, 1909–1919 (2014).

  43. 43.

    Colpitts, S. L. et al. Transcriptional regulation of IL-15 expression during hematopoiesis. J. Immunol. 191, 3017–3024 (2013).

  44. 44.

    Eckelhart, E. et al. A novel Ncr1-Cre mouse reveals the essential role of STAT5 for NK-cell survival and development. Blood 117, 1565–1573 (2011).

  45. 45.

    Marçais, A. et al. The metabolic checkpoint kinase mTOR is essential for IL-15 signaling during the development and activation of NK cells. Nat. Immunol. 15, 749–757 (2014).

  46. 46.

    Huntington, N. D. et al. Interleukin 15-mediated survival of natural killer cells is determined by interactions among Bim, Noxa and Mcl-1. Nat. Immunol. 8, 856–863 (2007).

  47. 47.

    Piccand, J. et al. Rfx6 maintains the functional identity of adult pancreatic β cells. Cell. Rep. 9, 2219–2232 (2014).

  48. 48.

    Deng, Y. et al. Transcription factor Foxo1 is a negative regulator of natural killer cell maturation and function. Immunity 42, 457–470 (2015).

  49. 49.

    Wang, S. et al. FoxO1-mediated autophagy is required for NK cell development and innate immunity. Nat. Commun. 7, 11023 (2016).

  50. 50.

    Feng, X. et al. Transcription factor Foxp1 exerts essential cell-intrinsic regulation of the quiescence of naive T cells. Nat. Immunol. 12, 544–550 (2011).

  51. 51.

    Narni-Mancinelli, E. et al. Fate mapping analysis of lymphoid cells expressing the NKp46 cell surface receptor. Proc. Natl Acad. Sci. USA 108, 18324–18329 (2011).

  52. 52.

    Bentzinger, C. F. et al. Skeletal muscle-specific ablation of raptor, but not of rictor, causes metabolic changes and results in muscle dystrophy. Cell. Metab. 8, 411–424 (2008).

  53. 53.

    Clausen, B. E. et al. Residual MHC class II expression on mature dendritic cells and activated B cells in RFX5-deficient mice. Immunity 8, 143–155 (1998).

  54. 54.

    Ludigs, K. et al. NLRC5 shields T lymphocytes from NK-cell-mediated elimination under inflammatory conditions. Nat. Commun. 7, 10554 (2016).

  55. 55.

    Held, W., Lowin-Kropf, B. & Raulet, D. H. Generation of short-term murine natural killer cell clones to analyze Ly49 gene expression. Methods Mol. Biol. 121, 5–12 (2000).

  56. 56.

    Jordan, S. et al. Virus progeny of murine cytomegalovirus bacterial artificial chromosome pSM3fr show reduced growth in salivary glands due to a fixed mutation of MCK-2. J. Virol. 85, 10346–10353 (2011).

  57. 57.

    Brune, W., Hengel, H. & Koszinowski, U. H. A mouse model for cytomegalovirus infection. Curr. Protoc. Immunol. 43, 19.17 (2001).

  58. 58.

    Zurbach, K. A., Moghbeli, T. & Snyder, C. M. Resolving the titer of murine cytomegalovirus by plaque assay using the M2-10B4 cell line and a low viscosity overlay. Virol. J. 11, 71 (2014).

  59. 59.

    Masternak, K., Peyraud, N., Krawczyk, M., Barras, E. & Reith, W. Chromatin remodeling and extragenic transcription at the MHC class II locus control region. Nat. Immunol. 4, 132–137 (2003).

  60. 60.

    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

  61. 61.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  62. 62.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

  63. 63.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  64. 64.

    Wasserman, W. W. & Sandelin, A. Applied bioinformatics for the identification of regulatory elements. Nat. Rev. Genet. 5, 276–287 (2004).

Download references


We thank M. Thome-Miazza and P. Schneider (UNIL, Lausanne); W. Reith (UNIGE Medical School, Geneva); M. Rüegg and M. Hall (Biozentrum, Basel); and M. Mazzone (VIB, Leuven) for sharing reagents important for this study. We thank N. Fonta, W. Held, R. Bedel, S. Siegert, S. Calderon, K. Harshman, and R. Hertzano for technical help. Studies in the group of G.G. were funded by the European Research Council (ERC-2012-StG310890) and the Swiss National Science Foundation (PP00P3_139094 and PP00P3_165833). S.P.M.W. is supported by the ETH post-doctoral fellowship program (FEL29 15-2) and the Horten Foundation. The laboratory of E.V. is supported by the ERC under the European Union’s Horizon 2020 research and innovation programme (grant agreement 694502), Agence Nationale de la Recherche, Innate Pharma, MSDAvenir, Ligue Nationale contre le Cancer (Equipe labelisée ‘La Ligue’) and Marseille-Immunopole. O.B. was supported by the Swiss National Science Foundation 310030-172978 and Swiss Cancer Research KFS-4136-02-2017. M.E.R. was supported by KFS-MDPhD-3557-06-2015. M.D. was supported by the Fondation Medic, Lausanne. P.-C.H. was supported by the Swiss National Science Foundation (31003A_163204), the Swiss Cancer League (KFS-3949-08-2016), and a Melanoma Research Alliance Young Investigator award.

Author information

Author notes

  1. These authors contributed equally: Wilson Castro, Sonia T. Chelbi.


  1. Department of Biochemistry, University of Lausanne, Epalinges, Switzerland

    • Wilson Castro
    • , Sonia T. Chelbi
    • , Charlène Niogret
    • , Cristina Ramon-Barros
    • , Kevin Osterheld
    • , Giorgia Rota
    • , Leonor Morgado
    •  & Greta Guarda
  2. Institute for Research in Biomedicine, Università della Svizzera Italiana, Bellinzona, Switzerland

    • Sonia T. Chelbi
    •  & Greta Guarda
  3. Institute of Microbiology, ETH Zürich, Zürich, Switzerland

    • Suzanne P. M. Welten
    •  & Annette Oxenius
  4. Ludwig Center for Cancer Research of the University of Lausanne, Epalinges, Switzerland

    • Haiping Wang
    • , Mauro Delorenzi
    •  & Ping-Chih Ho
  5. Department of Fundamental Oncology, University of Lausanne, Epalinges, Switzerland

    • Haiping Wang
    • , Mauro Delorenzi
    •  & Ping-Chih Ho
  6. Centre d’Immunologie de Marseille-Luminy, Aix Marseille Université, Inserm, CNRS, Marseille, France

    • Eric Vivier
  7. Service d’Immunologie, Hôpital de la Timone, Assistance Publique–Hôpitaux de Marseille, Marseille, France

    • Eric Vivier
  8. Innate Pharma Research Labs., Innate Pharma, Marseille, France

    • Eric Vivier
  9. Department of Immunology, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    • Miro E. Raeber
    •  & Onur Boyman
  10. Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland

    • Mauro Delorenzi
    •  & David Barras


  1. Search for Wilson Castro in:

  2. Search for Sonia T. Chelbi in:

  3. Search for Charlène Niogret in:

  4. Search for Cristina Ramon-Barros in:

  5. Search for Suzanne P. M. Welten in:

  6. Search for Kevin Osterheld in:

  7. Search for Haiping Wang in:

  8. Search for Giorgia Rota in:

  9. Search for Leonor Morgado in:

  10. Search for Eric Vivier in:

  11. Search for Miro E. Raeber in:

  12. Search for Onur Boyman in:

  13. Search for Mauro Delorenzi in:

  14. Search for David Barras in:

  15. Search for Ping-Chih Ho in:

  16. Search for Annette Oxenius in:

  17. Search for Greta Guarda in:


W.C., S.T.C., C.N., C.R.-B., S.P.M.W., K.O., H.W., G.R., and L.M. performed the experiments; D.B., S.T.C., and M.D. performed bioinformatics analyses; E.V., M.E.R., O.B., M.D., P.-C.H., and A.O. shared protocols, reagents, help, and advice; W.C., S.T.C., and G.G. designed the research, analyzed the data, and wrote the manuscript.

Competing Interests

E.V. is a cofounder, shareholder, and employee of Innate Pharma. Unrelated projects in the laboratory of G.G. are supported by OM-Pharma, Galenica, and the Novartis Foundation. Three unrelated projects in the laboratory of P.-C.H. are supported by Roche and Idorsia. O.B. is a shareholder in Anaveon AG. An unrelated project in the group of M.D. is supported by Merck KGaA, and M.D. owns stocks from the companies Novartis, Roche, and Idorsia. The other authors have no conflicting financial interest to declare.

Corresponding author

Correspondence to Greta Guarda.

Integrated supplementary information

  1. Supplementary Figure 1 Rfx7 phylogeny and conditional-knockout strategy.

    (a) Percent identity matrix of full RFX7 protein sequences generated with Clustal 2.1. (b) Phylogenetic tree based on full protein alignment (Neighbor-joining tree without distance corrections, Clustal 2.1); the bar line (distance scale) indicates the percentage of variation among species, i.e. 0.1 represent 10% difference between two sequences. (c) Alignment of RFX7 DNA Binding Domain for the indicated species (JalView). The compared protein sequences correspond to the following species: Human, Homo sapiens; Chimpanzee, Pan troglodytes; Gorilla, Gorilla gorilla; Macaque, Macaca mulatta; Orangutan, Pongo pygmaeus; Mouse, Mus musculus; Rat, Rattus norvegicus; Rabbit, Oryctolagus cuniculus; Guinea pig, Cavia porcellus; Pig, Sus scrofa; Cow, Bos taurus; Chicken, Gallus gallus; Xenopus, Xenopus tropicalis; Zebrafish, Danio rerio. (d) Scheme showing the targeting strategy for the conditional knockout of the murine Rfx7 gene. The vector was designed such as the 5’ loxP site is inserted upstream of exon 3 and the target region is 1.96 kb including exons 3-4. The loxP/FRT flanked Neo cassette is inserted downstream of exon 4. The selection cassette (Neo) was excised by crossing floxed mice with FLP deleter line. The two loxP sites shown allow Cre-mediated deletion of exons 3 (Ex 3) and 4 (Ex 4). PCR primers b/c discriminate wild type (wt) and floxed (fl) Rfx7 alleles and primers a/c wild type and knockout (ko) Rfx7 alleles. Arrows on diagram indicate PCR primer positions. PCR products obtained for each genotype are shown. LA: long arm, MA: middle arm, SA: short arm, FRT: flippase recognition target, Neo: neomycine cassette

  2. Supplementary Figure 2 Rfx7 deletion is highly efficient in immune cells.

    (a,b) Quantitative RT-PCR (qRT-PCR) data (relative to Polr2a) showing Rfx7 transcript abundance in MACS-sorted T and B cells (a), and in FACS-sorted NK lymphocytes (b) from Vav Rfx7fl/fl and Rfx7fl/fl mice. Results represent mean ± SD of technical replicates (n = 3) and are representative of at least two experiments (a,b)

  3. Supplementary Figure 3 Rfx7 deficiency affects NK cells in various tissues.

    (a) The abundance of Rfx7 mRNA was determined by qRT-PCR in the indicated subsets of sorted NK cells from BM or spleen (SP) of wild type mice (relative to Polr2a). (b) A representative flow cytometric plot of NK cells (gated as NK1.1+CD3CD19) in the blood and liver of Ncr Rfx7wt/wt, and Ncr Rfx7fl/fl mice is shown (gated on CD45+ lymphocytes). Results represent the mean ± SD of n = 3 technical replicates (a) or the mean ± SEM of n = 3 mice/group (b) and are representative of at least two independent experiments (a,b). (b) Statistical comparison between the experimental condition lacking Rfx7 and control were performed; *p ≤ 0.05; Student’s t-test

  4. Supplementary Figure 4 Validation of genes intrinsically regulated by Rfx7.

    (a) Control and Rfx7-deficient NK cells were isolated from BM (sorted as CD122+ NK1.1+) and spleen (sorted as Ncr1+ and NK1.1+) from a pool of nine Vav Rfx7 wt/wt:Vav Rfx7 fl/fl mixed BM chimeras. Transcript abundance of 12 genes, selected based on the RNA-sequencing results, was determined by qRT-PCR (relative to Polr2a). (b) MACS-sorted CD4+ and CD8+ T cells from spleens of Cd4 Rfx7wt/wt and Cd4 Rfx7fl/fl mice were tested for mRNA levels of the indicated genes by qRT-PCR (relative to Polr2a). (c) Expression of the indicated proteins in MACS-enriched T cells and T cell-depleted fractions (flow through, FT) from splenocytes of Vav Rfx7wt/wt or Vav Rfx7fl/fl mice was determined by immunoblot analysis. Actin was used as loading control. (d,e) Table illustrating the fold difference (KO:WT) detected in the RNA-sequencing for Rfx genes (d) or the indicated MHC-related genes (e) in BM or spleen NK cells. f) Graphs depict the geometric MFI of surface H2-D, H2-K, and Qa2 as detected on NK cells of the indicated genotypes and a representative histogram thereof. Results depict mean ± SD of n = 3 technical replicates per genotype/organ (a) or the mean ± SEM of n = 3 (Cd4 Rfx7wt/wt) and n = 5 (Cd4 Rfx7fl/fl) mice (b) and of n = 3 (Rfx7fl/fl), n = 3 (Vav Rfx7wt/wt) and n = 4 (Vav Rfx7fl/fl) mice (f). Results are representative of at least two independent experiments (a,b,c,f). (a,b,f) Statistical comparison between the experimental condition lacking Rfx7 and controls were performed; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001; Student’s t-test

  5. Supplementary Figure 5 Analyses of Rfx7-target genes in silico.

    (a) In Silico analysis of promoter sequences (-700; +300) of differentially expressed genes from cluster 1 (downregulated genes), cluster 2 (upregulated genes), and cluster 0 (non-modulated genes, serving as control group) was performed with JASPAR tool for the presence of putative transcription factor binding sites (TFBS) for CCAAT/enhancer binding protein beta (Cebpb) and T-box 15 (Tbx15). The box and whisker plots depict the number of putative TFBS identified with a relative profile score threshold of 0.8. Significance was determined using one-tail Mann-Whitney tests; *p ≤ 0.05

  6. Supplementary Figure 6 Rfx7-dependent regulation of size and granularity.

    (a) Quantification of forward scatter (FSC) and side scatter (SSC) for splenic NK cells (gated as NK1.1+ CD3/19-) from Ncr Rfx7wt/wt (set as 100%) and Ncr Rfx7fl/fl mice. (b) A representative ImageStream picture of NK cells (bright field or anti-NK1.1 staining) from Ncr Rfx7wt/wt and Ncr Rfx7fl/fl. Histogram illustrates the surface area based on the bright field of 30’000 to 100’000 cells per mouse and genotype and the graph a quantification thereof (each line colored in hues of red or blue represents one mouse, Ncr Rfx7wt/wt and Ncr Rfx7fl/fl, respectively). (c,d) FSC and SSC for BM NK cells (gated as CD122+ CD3/19) are shown for Vav Rfx7wt/wt, and Vav Rfx7fl/fl mice (c) and for Ncr Rfx7wt/wt and Ncr Rfx7fl/fl mice (d). (e) Quantification of FSC and SSC for splenic CD4+ T cells (gated as CD3+CD4+), CD8+ T cells (gated as CD3+CD8+), NKT cells (gated as NK1.1+CD3+), NK cells (gated as NK1.1+CD3), B cells (gated as CD19+), and conventional dendritic cells (DC; gated as CD11chiCD11bint–hi) from the indicated mice. (f) HEK293T cells were co-transfected with WT, NLS-mutant (m658 & 674) Rfx7, or empty (mock) and GFP-encoding vectors. After 48 hours, FSC was analyzed on the GFP+ cells, with the FSC of mock-transfected cells set at 100. (g) Quantification of FSC and SSC for splenic NK cells, CD8+ T cells, and B cells from Rfx5+/+, Rfx5+/, and Rfx5/ mice. (h) The graph depicts ratios of Rfx7-deficient to control living NK cells (congenically marked) co-cultured in the presence of high IL-15 and S63845 (Mcl-1 inhibitor; Cayman Chemical) for three days (normalized to initial mix). Flow cytometry plots illustrate dead cell percentage. Results represent the mean ± SEM of n = 6 (Ncr Rfx7wt/wt) and n = 7 (Ncr Rfx7fl/fl) (a,d), n = 5 (Ncr Rfx7wt/wt) and n = 8 (Ncr Rfx7fl/fl) (b), n = 5 (Vav Rfx7wt/wt) and n = 6 (Vav Rfx7fl/fl) (c), n = 8 (Rfx7fl/fl), n = 10 (Vav Rfx7wt/wt), and n = 9 (Vav Rfx7fl/fl) (e), n = 4 (Rfx5+/+), n = 7 (Rfx5+/), and n = 6 (Rfx5/) mice (g) or mean ± SD of n = 4-5 technical replicates per condition (f,h) and are representative of at least two independent experiments (a-f) and a pool of two independent experiments (g,h). Statistical comparison between the experimental condition and controls were performed; ***p ≤ 0.001; Student’s t-test (a-g)

  7. Supplementary Figure 7 Rfx7-deleted NK cells present only moderate alterations in functional features.

    (a) Expression analysis of the indicated receptors on splenic NK cells (NK1.1+CD3CD19) from Vav Rfx7wt/wt, and Vav Rfx7fl/fl mice is depicted as percentage of positive population (for biphasic expression) or geometric MFI. (b) Percentages and numbers of NK cells (gated as NK1.1+ and CD3/19) in the spleen of Ncr Rfx7fl/fl and Ncr Rfx7wt/wt mice treated with IL-2 complexes (cIL-2) for four days and used for B2m–/– splenocyte rejection on day 5 (presented in Fig. 8c) are depicted. (c) A representative flow cytometric plot and a quantification of IFNγ and granzyme A production by splenic NK cells of Rfx7fl/fl, Vav Rfx7wt/wt, and Vav Rfx7fl/fl mice are shown. Results represent the mean ± SEM of n = 5 (Vav Rfx7wt/wt) and n = 4 (Vav Rfx7fl/fl) (a) or n = 4 (Ncr Rfx7wt/wt) and n = 5 (Ncr Rfx7fl/fl) mice (b), or n = 4 (c) and are representative of at least two independent experiments (a-c). Statistical comparison between the experimental condition lacking Rfx7 and controls were performed; *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001; Student’s t-test

Supplementary information

  1. Supplementary Figures

    Supplementary Figures 1–7 and Supplementary Tables 1 and 2

  2. Reporting Summary

About this article

Publication history